Forecasting Monthly Turnover in BIG COMPANY Call Center Positions:
April, 2005 to March, 2006
Overview and Caveats
Analyses reported below estimate the nature of the relationship between 1) ABC
Consulting assessments and 2) length of subsequent job tenure among recent
applicants for call center positions at BIG Company. The forecasts are expected
to be accurate to the extent that 1) relationships between ABC Consulting
assessments and subsequent job tenure are found and 2) future applicants for
call center positions are drawn from the same applicant pool population that past
applicants were drawn from. Specifically, using each successful applicant’s
assessment score profile, the model forecasts how many days s/he is likely to
stay on the job.
Forecasts were made regarding how many of the successful applicants hired
since June, 2003 will still be employed in the months making up the second and
third quarters of 2005 (i.e., April through September, 2005). Median job tenure of
those hired between June, 2003 and March, 2005 and who subsequently turned
over was 80 days. Figure 1 below shows the job tenure frequency distribution of
those who turned over. Visual interpretation of the frequency distributions
suggests the highest risk of turnover occurs in the first 120 days (70% turnover
within 120 days, while 80% turned over within 180 days or 6 months). Further,
forecasted turnover dates for individuals with more than 6 months of job tenure
(i.e., hired prior to October 1, 2004) will likely be inaccurate, as causes of
turnover after 6 months of employment appear to be fundamentally different from
causes of turnover during the first 6 months of employment. For example, while
median job tenure was 80 days for those who turned over, those who turned over
by failing to return from leave (N = 15) was 179 days and for violations of
rules/insubordination (N = 81) was 214 days.
Hence, assuming BIG COMPANY is constantly hiring to refill positions as
turnover occurs, turnover forecasts beyond 180 days into the future cannot be
made with any accuracy from the current data (either because the employees
most likely to turnover in October, November, and December of 2005 have not
yet been hired or because causes of turnover are more difficult to predict the
longer a person spends on the job). Forecasts of future turnover rates in this
report are limited to the 6 months occurring between April and September, 2005.
Figure 1: Job tenure frequency for those who turned over, June 2003 to February, 2005
0.2
150
100
0.1
50
0
0
100
200 300 400
# of Days on the Job
500
Proportion per Bar
# of Incumbents Still on the Job
200
0.0
600
Regardless, some caveats about these predictions must be kept in mind.
Forecasts for future months will decrease in accuracy relative to forecasts made
on historic data (i.e., the data obtained on successful applicants between June,
2003 and March, 2005 used to estimate the model) if some fundamental changes
occur in the nature of the labor market(s) or how BIG Company (or its
competition) draws applicants from those markets. Specifically, changes in
recruiting activities (by BIG COMPANY or its labor market competitors), changes
in applicant demand (by BIG COMPANY or its labor market competitors),
changes in applicant supply (quality or quantity), or any other factor that might
influence the depth or quality of the applicant pool could cause turnover forecasts
to become less accurate.
Note, the traditional metric of prediction accuracy for least squares multiple
regression is the multiple correlation coefficient RY . X1 X 2 ... X k , where Y is the criterion
or dependent variable to be predicted and X1 to Xk are the predictor or
independent variables. Unfortunately, RY . X1 X 2 ... X k is greatly influenced (generally
reduced) by a number of characteristics of how a study and subsequent analyses
were conducted. Use of a personnel selection system in selecting among
applicants (i.e., generally selecting those with higher scores) results in reduced
variability in X1 through Xk among newly hired applicants because applicants with
low values of X1 through Xk were simply not hired. Restriction in range of X1
through Xk causes estimates of RY . X1 X 2 ... X k to be lower than they would have been
if the range of X1 through Xk had not been restricted (i.e., if all applicants had
been hired with no consideration given to their assessment scores). Portions of
the current sample were selected on the basis of scores generated by ABC
Consulting solutions, while scores on ABC Consulting solutions were not
generated or given consideration in selection of other portions of the sample.
Hence, the current data do not permit good estimation of how accurate the model
is in predicting future turnover. A conservative estimate of accuracy in turnover
prediction comes from an earlier report (Beaty, 2004). The best, most accurate,
estimate of prediction accuracy will be reflected in the correlation between the 1)
predicted estimates of job tenure ( $y in days) and 2) actual job tenure observed
(y) for remaining employees and recruits who are newly hired over the next 6
months.
Method
Predictors
Turnover was predicted using two types of information. The first was derived
from items from the ABC Consulting solution administered to applicants for BIG
COMPANY call center positions between June 6, 2003 and March 11, 2005.
These items are listed below. Each item was accompanied by a 5-point
response scale, yielding 47 x 5 = 235 separate response options used as
predictors in the current study.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
Being on time for work is not as important as some people say it is.
Even when I am very upset, it is easy for me to control my emotions.
Having goals and quotas makes work more exciting.
Having my telephone calls with clients recorded or having my supervisor listen in
wouldn't bother me at all.
How many jobs have you held in the last 5 years?
How much experience do you have working in a call center (centre)?
I am able to maintain a standard work schedule with the same start, stop, break, and
lunch times each week.
I am comfortable multi-tasking -- such as accessing multiple computer screens, while
talking on the phone and answering customer questions.
I am known for being committed to my work.
I am looking for entry level positions with a great company to 'get my foot in the door'.
I can learn many new things in a relatively short period of time.
I can usually stay calm, even in stressful situations.
I could adhere to a strict work schedule.
I do whatever is necessary to improve my chances of advancing beyond my current
position.
I do whatever it takes to make people happy.
I don't enjoy having to make others happy.
I easily adapt to changes and new ways of doing things.
I enjoy working in a fast-paced environment.
I expect repetition in this job; doing the same thing every day wouldn't bother me at all.
I have a strong desire to exceed expectations rather than just succeed.
I must admit that I often lose my temper.
I need some time to adapt to new situations.
I would enjoy accepting customer calls throughout the entire day with little opportunity for
socializing with my co-workers.
I would enjoy receiving job performance feedback from my supervisor on a regular basis.
I would enjoy talking to customers on the phone all day.
26. I would enjoy working in a highly structured environment where my breaks and schedules
are fixed.
27. I would enjoy working in a job where I constantly had to learn new things.
28. I would like a job where I talk on the phone to customers all day.
29. I would like to attain the highest position in an organization someday.
30. I would not enjoy dealing with customers who are angry or get frustrated easily.
31. I wouldn't mind having my performance monitored very closely.
32. If we asked your last supervisor or teacher, how would he/she rate your ability to meet
goals or complete assignments?
33. If we asked your last supervisor or teacher, how would he/she rate your ability to quickly
learn large amounts of information?
34. In difficult situations, I can think about a problem calmly.
35. In school and/or at my previous job, I often took it upon myself to learn more than my
classmates/coworkers.
36. In school or at work, I usually ask my teacher/supervisor for feedback on my
performance.
37. In school or at work, I usually learn new things much faster than others.
38. In stressful situations, I generally remain calm and composed.
39. It takes a lot for someone to hurt my feelings.
40. Other people get on my nerves a lot.
41. People I know would say that I have a lot of patience.
42. People often tell me about their problems and feelings.
43. People say that I am flexible.
44. When given an assignment or goal, I ALWAYS do more than what's expected of me.
45. When someone with authority tells me to do something, I always do it.
46. Why are you interested in this position?
47. Working for a large company is an important part of my career goals.
A predictor score was derived by empirically keying the response options.1
Second, seasonal turnover trends had been noted by BIG COMPANY in the
past. Hence, the four quarters within a calendar year where dummy coded (i.e.,
1 = 1st 3 months of year, 2 = 2nd 3 months of year, etc.).
Criterion
The primary criterion used in analyses reported below is labeled “job tenure.” It
deserves brief mention because it is known to contain a particular kind of
inaccuracy. Specifically, all employees will turnover sooner or later due to
voluntary (e.g., leaves for a better job elsewhere, promoted internally, retirement,
etc.) or involuntary (e.g., fired for cause, laid off, death, etc.) reasons. Simply
measuring turnover as a dichotomous variable where 0 = turned over and 1 = not
turned over results in loss of information, e.g., it fails to distinguish between those
who turned over in their 3rd week and those who turned over in their 3rd year. Job
tenure, a simple count of the number of days between date of initial employment
1
Each response option was initially correlated with job tenure for those who had turnover and for all
applicants. Predictor scores = sum of correlations between response options selected by each applicant.
Predictor scores did not differ meaningfully based on whether they had been derived from 1) applicants
who had turned over versus 2) all applicants. The predictor score derived from correlations based on only
those applicants who had turned over was used here.
and date of turnover, recaptures that lost information while simultaneously
injecting a new source of systematic measurement error.
The systematic error occurs because most studies of turnover, including this one,
use employee samples that include both individuals who have turned over and
individuals who have yet to turnover (but who will at some unknown point in the
future). The job tenure of those who have turned over is accurately known. The
job tenure of those who have yet to turnover cannot be known with certainty. All
one knows for sure is that their job tenure will be at least one day longer then the
difference in days between the date on which turnover data was gathered and
their start date. Hence, while the true job tenure measure Y for these individuals
will be the number of days between their hire date and (future) turnover date, a
conservative estimate of job tenure for those who have yet to turnover is “Date of
data acquisition – Hire date + 1.” This is how the “job tenure” measure was
operationalized for analyses reported below.2
Analyses, Results, and Discussion
Job tenure was regressed onto 1) the predictor score derived from ABC
Consulting assessments and 2) the seasonal dummy variable. When this was
done for just those applicants in the sample who had actually turned over, RY  X1 X 2
= .13 (p < .01), though the regression coefficient for the season dummy variable
was not significantly different from zero. When the same analysis was done on
all applicants in the sample (i.e., including those who had yet to turnover),3
RY  X1 X 2 = .15 (p < .01) and the season dummy variable became significant. The
difference in contribution of the seasonality factor suggests something different
was contributing to prediction of applicants decisions to stay on the job versus
leave early.
Consequently, comparisons were made of the average job tenure associated
with each stated “reason for turnover.” Table 1 reports descriptive statistics from
this comparison.
2
A separate model was derived regressing job tenure onto predictor variables for just those individuals in
the data who had turned over, i.e., on just those individuals on whom we had accurate job tenure data.
Predictions made by this model were trivially different from those made by the model estimated on all
individuals in the sample, though the smaller sample size (N = 748 versus N = 1348) caused prediction
intervals to be wider around those forecasts.
3
Recall, the job tenure measure for those who had yet to turnover was set equal to the number of days
between March 13, 2005 and their date of hire. Data was acquired on March 12, 2003, hence this
computation implicitly assumes all those still employed were at least still employed the next day.
Table 1: Job Tenure by Reason for Turnover
Job
Tenure
Excessive
Absences
Poor
Perform
Violation of
Rules
Failure to
Return
from Leave
Failed
Background
Check
Resigned
N
Mean
Median
SD
70
104
74
89
112
86
94
50
81
176
176
102
15
214
169
139
13
18
15
22
646
109
74
97
Recall the median job tenure among all those who turned over was 80 days, with
70% turning over within 120 days. Results reported in Table 1 suggest those
who turn over after 120 days do so for substantively different reasons (i.e.,
Violation of Rules/Insubordination and Failure to Return from Leave) compared
to those turning over within the first 4 months on the job. Curiously, interpretation
of the significant “season” dummy variable suggests those who have not turned
over yet tended to be hired earlier in the year (winter and spring). Combined,
these findings suggest those “stayers” who remain on the job or turnover late (>
120 days) on the job do so for substantively different reasons than those who
turnover early (< 120 days) on the job.
As most turnover occurs within the first 120 days of employment, all forecasts
below were made only for those individuals hired in the last 6 months (i.e., since
September, 2004). Prediction of turnover for those “stayers” hired prior to
September, 2004, cannot be as accurate due to 1) the systematic measurement
error contained in their job tenure noted above (i.e., job tenure is necessarily a
conservative downside estimate for those who haven’t turned over yet), 2)
smaller N, and 3) the apparent fact that different things influence their turnover,
causing their response option → turnover relationships to differ from those who
turnover within 120 days.
Forecasts of future turnover dates for each successful applicant were made from
the multiple regression equations reported above. Given the prior conclusion
that those who haven’t turned over and/or who turned over after 120 days of job
tenure do so for different reasons, it is not surprising that forecasts differed for
the two prediction models. Specifically, forecasts made from a model derived
from all applicants hired between June, 2003 and March, 2005 yielded an
average expected job tenure of 179 days. Forecasts made from a model derived
from just those applicants who had turned over during this period yielded an
expected average job tenure of 110 days. Unfortunately, we cannot know which
of the current employees are likely to be “quick turnovers” (i.e., those who
turnover in less than 120 days) versus “stayers” (i.e., those who stay longer than
120 days and, when they do turnover, do so for different reasons).4 Hence, for
purposes of prediction, Table 2 presents forecasted turnover frequency for the
Note, additional analyses were performed to determine whether “early leaver” versus “stayer” status could
be predicted. Significant prediction of this coarse, artificially dichotomized turnover outcome did not
occur.
4
next 6 months drawn from 1) a model derived from just those who had turned
over (Model A), 2) all applicants hired between June, 2003 and March 11, 2005
(Model b), and 3) an average of the Models A and B. Note, Model A forecasts
are particularly low because it predicts most individuals hired since September,
2004, would have turned over some time prior to April 1, 2005. In fact, many did,
though because Table 2 only makes forecasts for those who are still employed,
they are not included in Table 2’s forecasted future turnover counts.
Table 2: Predicted Turnover for New Hires Remaining since September,
2004
Model Model
A2
B3
Salt Lake City
(N = 13)
Average1
Phoenix
(N = 45)
Average1
Average1
Model Model
A2
B3
Greensboro
(N = 3)
Model Model 2
Model
A
A2
B3
34
33
10
27
6
4
6
17% 23% 7% 19%
0
0
133% 13% 9% 13%
0
50
12
19
45
5
15
5
May, 2005
25% 8% 13% 31%
0
0
0
11% 33% 11%
0
29
16
4
23
13
6
6
11
June, 2005
14% 11% 3% 16%
0
33%
0
29% 13% 13% 85%
20
19
1
11
9
6
2
July, 2005
10% 13% 1%
8%
0
0
0
20%
0
13% 15%
45
1
21
1
1
14
August, 2005
0
22% 1%
0
15% 33%
0
0
2%
0
31%
0
September,
9
3
1
6
2005
0
0
4%
0
0
2%
0
0
33%
0
0
13%
0
1. Average month of turnover based on average forecasted job tenure of Models A and B.
2. Model A derived from only those individuals who were hired and turned over between June, 2003
and March 12, 2005.
3. Model B derived from all individuals hired between June, 2003 and March 12, 2005.
April, 2005
39
19%
17
8%
40
20%
30
15%
2
1%
Model Model
A2
B3
Ft. Lauderdale
(N = 145)
Average1
Forecast
Period
Average1
Predicted #
Turning Over if
Hired Since
9/1/2004
(N = 206)
17
8%
44
22%
10
5%
1
1%
Next, forecasts made in Table 2 were broken split out by location. The last 12
columns of Table 2 contain forecasted turnover frequencies. Table 3 below
contains descriptive statistics for those who were both hired and turned over
between June, 2003 and March, 2005.
Table 3: Descriptive Statistics for Job Tenure by Location
N of cases
Minimum
Maximum
Median
Mean
SD
Ft. Lauderdale
449
0
529
77
105.1
93.7
Greensboro
274
0
526
85
119.3
101.5
Phoenix
224
0
478
87
117.0
95.8
Salt Lake City
3
130
298
284
237.3
93.2
Model Model 2
Model
A
A2
B3
3
23%
10
77%
0
0
0
0
0
3
23%
10
77%
0
0
0
Finally, relationships between recruiting source and job tenure were examined.
Table 4 contains job tenure descriptive statistics for each recruiting source.
Curiously, Past Employees have the lowest median job tenure (mean job tenure
is highly influenced by extreme values in the data, hence, medians are a better
index of central tendency). Applicants referred from the Arizona Republic and
Yahoo.com where the only source of applicants with median job tenure greater
than 100 days for those who had already turned over. AOL and Monster.com
had the highest median job tenure for those who had yet to turnover, while the
job tenure of their recruits who had turned over was fairly short (65 and 67 days,
respectively).
Table 4: Job Tenure for First Measure of Recruiting Source1
SOURCE
N
Minimum
Maximum
Median
Mean
SD
SOURCE
N
Minimum
Maximum
Median
Mean
SD
SOURCE
N
Minimum
Maximum
Median
Mean
SD
Research
Turned
Over
4
18
106
75.5
68.8
41.9
Yet to
Turnover
2
138
138
128
138
0
Walk In
Turned
Over
22
0
350
93.5
113.0
100.8
Yet to
Turnover
16
96
537
316.5
302.3
103.2
Internet
Turned
Over
216
0
529
78.0
114.6
101.2
Job Fair
Turned
Over
22
17
341
82.5
110.2
91.4
Yet to
Turnover
26
19
523
295
288.7
106.3
Yet to
Turnover
28
26
544
327
314.0
127.1
Past Employee
Turned
Over
12
9
158
58.5
62.7
40.9
Yahoo
Arizona Republic
Turned
Over
39
0
310
102
117.6
83.9
Yet to
Turnover
164
12
579
313
294.5
126.5
Referral from BIG
COMPANY
Turned
Yet to
Over
Turnover
129
91
0
12
393
551
78.0
320
103.6
304.0
86.0
133.9
Turned
Over
12
29
314
127
150.9
107.3
Yet to
Turnover
13
12
397
264
228.8
141.1
Yet to
Turnover
15
47
411
229
204.7
124.3
AOL
Turned
Over
11
26
347
65
139.9
112.8
Yet to
Turnover
11
103
425
341
310.0
103.4
Advert.
Turned
Over
183
0
524
85.0
113.5
02.3
Yet to
Turnover
131
19
558
250
247.3
134.2
College
Turned
Over
12
12
227
96.5
97.1
59.8
Yet to
Turnover
8
103
411
337.5
296.4
115.7
Miami Herald
Turned
Over
16
16
413
96
117.5
95.6
Yet to
Turnover
7
47
495
320
265
167.4
SOURCE
N
Minimum
Maximum
Median
Mean
SD
1.
Monster
Turned
Over
61
2
529
67
101.0
108.2
Yet to
Turnover
40
26
537
358.5
340.8
121.9
Employment Guide
Turned
Over
35
0
367
78
96.1
79.9
Yet to
Turnover
34
26
523
152
191.9
121.6
Other
Career Builder
Turned
Over
55
4
412
84
112.5
93.5
Yet to
Turnover
30
47
523
288
286.0
136.5
Turned
Over
94
0
393
78
105.2
94.2
Yet to
Turnover
75
12
570
278
260.7
130.0
Note, some of these categories are subsequently broken down into smaller subcategories (e.g. Advertising contains figures reported separately for the Arizona Republic,
Miami herald, and Employment Guide). Further, only sources with N > 10 were reported.
Future Directions
It remains to be seen which Model (or the average of the two) will predict best.
One might expect some weighted combination of Model A & B predictions would
be preferred. As actual turnover is incurred over the next 6 months, comparisons
can be made to determine which Model or weighted/unweighted combination of
the models predicts best. In addition, once the preferred forecasting model is
identified, BIG COMPANY might want to consider commissioning development of
a spreadsheet tool to make ongoing rolling turnover forecasts as new hires
occur. One such spreadsheet was developed to make the forecasts contained in
Table 2 above. With modifications, this spreadsheet could be used as a Rolling
Turnover Forecasts document generated in real time showing BIG COMPANY
human resources personnel what the expected turnover frequencies are going to
be in the immediate future, helping them make decisions about allocation of
recruiting resources, etc.
References
Beaty, J. (2004, September). Phase 1 Evaluation of New Job-Fit and Cognitive
Biodata Assessments for BIG Company Call Centers. ABC Consulting, Inc.